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Factors Affecting Crash Severity among Elderly Drivers: A Multilevel Ordinal Logistic Regression Approach

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  • Mubarak Alrumaidhi

    (Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA 24061, USA)

  • Hesham A. Rakha

    (Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA 24061, USA)

Abstract

This study modeled the crash severity of elderly drivers using data from the state of Virginia, United States, for the period of 2014 through to 2021. The impact of several exogenous variables on the level of crash severity was investigated. A multilevel ordinal logistic regression model (M-OLR) was utilized to account for the spatial heterogeneity across different physical jurisdictions. The findings discussed herein indicate that the M-OLR can handle the spatial heterogeneity and lead to a better fit in comparison to a standard ordinal logistic regression model (OLR), as the likelihood-ratio statistics comparing the OLR and M-OLR models were found to be statistically significant, with p -value of <0.001. The results showed that crashes occurring on two-way roads are likely to be more severe than those on one-way roads. Moreover, the risks for older, distracted, and/or drowsy drivers to be involved in more severe crashes escalate than undistracted and nondrowsy drivers. The data also confirmed that the consequences of crashes involving unbelted drivers are prone to be more severe than those for belted drivers and their passengers. Furthermore, the crash severity on higher-speed roads or when linked to high-speed violations is more extreme than on low-speed roads or when operating in compliance with stated speed limits. Crashes that involve animals are likely to lead to property damage only, rather than result in severe injuries. These findings provide insights into the contributing factors for crash severity among older drivers in Virginia and support better designs of Virginia road networks.

Suggested Citation

  • Mubarak Alrumaidhi & Hesham A. Rakha, 2022. "Factors Affecting Crash Severity among Elderly Drivers: A Multilevel Ordinal Logistic Regression Approach," Sustainability, MDPI, vol. 14(18), pages 1-12, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11543-:d:915021
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    References listed on IDEAS

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    1. Seunghoon Kim & Youngbin Lym & Ki-Jung Kim, 2021. "Developing Crash Severity Model Handling Class Imbalance and Implementing Ordered Nature: Focusing on Elderly Drivers," IJERPH, MDPI, vol. 18(4), pages 1-23, February.
    2. Gholamreza Shiran & Reza Imaninasab & Razieh Khayamim, 2021. "Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques, and Artificial Neural Network: A Modeling Comparison," Sustainability, MDPI, vol. 13(10), pages 1-23, May.
    3. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
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    Cited by:

    1. Wei Zhai & Shuqi Gao & Mengyang Liu & Di Wei, 2023. "Examining the effects of climate change perception and commuting experience on the willingness to pay for micro-transit service in Tampa, FL," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.
    2. Mubarak Alrumaidhi & Mohamed M. G. Farag & Hesham A. Rakha, 2023. "Comparative Analysis of Parametric and Non-Parametric Data-Driven Models to Predict Road Crash Severity among Elderly Drivers Using Synthetic Resampling Techniques," Sustainability, MDPI, vol. 15(13), pages 1-30, June.

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